Investigation of wheel squeal noise under mode coupling using two-disk testrig experiments.

Wheel squeal noise is a common problem that occurs in railway systems especially on curved tracks. These tonal noises are usually found at high frequency, typically over 1 kHz. The cause of the squeal noise remains unclear and could be different under varied conditions. Several mechanisms that could cause wheel squeal, including falling friction and mode coupling, have been proposed in literature. Most existing lab experimental investigations were focused on the squeal noise caused by falling friction with vertical contacts. As a result, the squeal noise generated by mode coupling could be missed due to the absence of a non-vertical contact angle. In this work, a two-wheel test rig has been designed with curved wheel profiles to achieve adjustable contact angles with lateral components. Experimental and numerical modal analysis of the two-disk system was conducted to determine the dynamic characteristics for exploration of mode coupling effects. Using a set of benchmark parameters predicted to be the cause of significant mode-coupled instability, squeal noise was found at frequencies higher than the existing falling friction experimental tests as reported in the literature. The frequency and amplitude measured in the experiments agreed with the predictions of the analytical mode coupling model. Further experiments varying control parameters, contact angle, angle of attack and direction of rotation were performed, confirming that the new squeal noise was caused by mode coupling. • The first experimental verification of mode coupling wheel squeal mechanism using two-disk testrig. • Experimental generated squeal noise level and frequency agreed well with the analytical model prediction. • Additional experiment with reversed rotation confirmed the identified squeal noise was caused by mode coupling. • Further parametric experiments varying contact angle and angle of attack were conducted, and the results were compared with the analytical model prediction and literature. [ABSTRACT FROM AUTHOR]